# flake8: noqa
# There's no way to ignore "F401 '...' imported but unused" warnings in this
# module, but to preserve other warnings. So, don't check this module at all.

# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_flax_available, is_torch_available, is_vision_available


_import_structure = {
    "configuration_beit": ["BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BeitConfig"],
}

if is_vision_available():
    _import_structure["feature_extraction_beit"] = ["BeitFeatureExtractor"]

if is_torch_available():
    _import_structure["modeling_beit"] = [
        "BEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "BeitForImageClassification",
        "BeitForMaskedImageModeling",
        "BeitForSemanticSegmentation",
        "BeitModel",
        "BeitPreTrainedModel",
    ]


if is_flax_available():
    _import_structure["modeling_flax_beit"] = [
        "FlaxBeitForImageClassification",
        "FlaxBeitForMaskedImageModeling",
        "FlaxBeitModel",
        "FlaxBeitPreTrainedModel",
    ]

if TYPE_CHECKING:
    from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig

    if is_vision_available():
        from .feature_extraction_beit import BeitFeatureExtractor

    if is_torch_available():
        from .modeling_beit import (
            BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            BeitForImageClassification,
            BeitForMaskedImageModeling,
            BeitForSemanticSegmentation,
            BeitModel,
            BeitPreTrainedModel,
        )

    if is_flax_available():
        from .modeling_flax_beit import (
            FlaxBeitForImageClassification,
            FlaxBeitForMaskedImageModeling,
            FlaxBeitModel,
            FlaxBeitPreTrainedModel,
        )


else:
    import sys

    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
